Panel Data - Memorial University of Newfoundland

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ONE PROBLEM: Even with the trick of using the within estimator, we still implicitly (even if no longer explicitly) include N-1 dummy variables in our model (not N, since we remove the intercept), so we use up N-1 degrees of freedom. It might not be then the most efficient way to estimate the common slope ANOTHER ONE. By using deviations from the means, the procedure wipes out all the static variables, whose effects might be of interest In order to overcome this problem, we can consider the random effects/or error components model Principles of Econometrics, 3rd Edition Slide 15-36
• In the RE model, the individual firm differences are thought to represent a random variation about some average intercept for the individual in the sample • Rather than a separate fixed effect for each firm, we now estimate an overall intercept that represents this average • Implicitly, the regression function for the sample firms vary randomly around this average. • The variability of the individual effects is captured by a new parameter, which is the variance of the random effect. • The larger this parameter is, the more variation you find in the implicit regression functions for the firms. Principles of Econometrics, 3rd Edition
Page 1 and 2: ECON 4551 Econometrics II Memorial
Page 3 and 4: The different types of panel data s
Page 5 and 6: INV =β +β V +β K + e t = 1, , 20
Page 7 and 8: ( ) ( ) 2 ( ) E e = 0 var e =σ cov
Page 9 and 10: • Let D i be a dummy variable equ
Page 11 and 12: ( ) cov e , e = σ GE, t WE, t GE,
Page 13 and 14: Principles of Econometrics, 3rd Edi
Page 15 and 16: In GRETL the restrict command can b
Page 17 and 18: There are two situations where sepa
Page 19 and 20: Testing for correlated errors for t
Page 21 and 22: Testing for correlated errors for M
Page 23 and 24: • SUR is OK when the panel is lon
Page 25 and 26: ⎧1 i= 1 ⎧1 i= 2 ⎧1 i= 3 D1 i
Page 27 and 28: H H : β =β = =β 0 11 12 1N : th
Page 29 and 30: F = ( R − U) SSE ( NT − K ) SSE
Page 31 and 32: y =β +β x +β x + e it 1i 2 2it 3
Page 33 and 34: Usually, there is no interest in th
Page 35: y = b + bx + bx i 1i 2 2i 3 3i b1 i
Page 39 and 40: ( ) y =β +β x +β x + e + u it 1
Page 41 and 42: But now there are several correlati
Page 43 and 44: • The correlation between errors
Page 45 and 46: y =β +β x +β x + e it 1 2 2it 3
Page 47 and 48: GRETL shows by default this Breusch
Page 49 and 50: y =β x +β x +β x + v * * * * * i
Page 51 and 52: • There are different ways to cal
Page 53 and 54: • Note that there is within varia
Page 55 and 56: • The RE uses information from bo
Page 57 and 58: If the random error v = u + e it i
Page 59 and 60: y =β +β x +β x + u + e it 1 2 2i
Page 61 and 62: The test statistic to the coefficie
Page 63 and 64: Further issues We can generalise th
Page 65 and 66: Further issues • Of course it is
Page 67 and 68: Further issues • You can understa
Page 69 and 70: Readings Wooldridge’s book on pan
Page 71 and 72: Principles of Econometrics, 3rd Edi
Page 73 and 74: yi =β 1+β 2x2i +β 3x3i + ui + ei

Panel Data - Memorial University of Newfoundland

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